System

Abstract

An object of a system according to an embodiment is to remove language barriers and realize smooth communication.SOLUTION: A system according to an embodiment includes a translation unit, a voice generation unit, and an emotion estimation unit. The translation unit translates an input of a user. The speech generation unit converts the result translated by the translation unit into speech. The emotion estimation unit estimates an emotion of a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making communication difficult due to language barriers.

[0005] The system according to the embodiment aims to remove language barriers and realize smooth communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a translation unit, a speech generation unit, and a feeling estimation unit. The translation unit translates a user's input. The speech generation unit converts the result of the translation by the translation unit into speech. The feeling estimation unit estimates the user's feeling. [Effects of the Invention]

[0007] The system according to the embodiment can remove language barriers and achieve smooth communication. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The communication support system according to the embodiment of the present invention is a system that uses generative AI to remove language barriers and enable people all over the world to communicate smoothly. As a result, the communication support system can remove language barriers and support users so that they can be active all over the world.

[0029] A communication support system according to an embodiment includes a translation unit, a speech generation unit, and an emotion estimation unit. The translation unit translates a user's input. For example, the translation unit translates text input by a user into another language. The translation unit can also translate speech input by a user into another language. The translation unit can also translate the input language in real time using a generation AI. For example, the generation AI translates the input text into another language using a text generation AI (e.g., LLM). The speech generation unit converts the result of the translation by the translation unit into speech. For example, the speech generation unit uses speech synthesis technology to convert text into speech. The speech generation unit can also convert the translation result into natural-sounding speech using the generation AI. For example, the generation AI converts the translation result into speech using a multimodal generation AI. The emotion estimation unit estimates the user's emotion. For example, the emotion estimation unit analyzes the user's facial expression to estimate the emotion. The emotion estimation unit can also analyze the user's speech to estimate the emotion. The emotion estimation unit can also estimate emotions from a user's text using a generation AI. For example, the generation AI estimates emotions from a user's text using a text generation AI. This allows the communication support system according to the embodiment to remove language barriers and achieve smooth communication.

[0030] The translation unit can learn the user's pronunciation and intonation and generate more natural translated speech. For example, the translation unit uses a generation AI to learn the user's pronunciation and intonation and generate translated speech optimized for each individual user. For example, the translation unit can learn the unique intonation used when a user speaks Japanese and reflect that intonation when translating into English. The translation unit can also use the generation AI to learn the user's pronunciation and intonation in real time and reflect it in the translated speech. This allows the translation unit to learn the user's pronunciation and intonation and generate more natural translated speech, thereby improving the quality of communication.

[0031] The translation unit can take cultural nuances and idiomatic expressions into consideration and provide more appropriate expressions. For example, the translation unit uses a generative AI to learn cultural nuances and idiomatic expressions and provide appropriate expressions when translating. For example, translating the Japanese phrase "otsukaresama" into English "Good job." The translation unit can also use generative AI to perform translations that take cultural background into consideration. For example, the generative AI references a database of cultural background and provides an appropriate translation. This allows for more appropriate translations to be provided by taking cultural nuances and idiomatic expressions into consideration, improving the quality of communication.

[0032] The communication support system is equipped with a sign language translation unit that translates sign language and visual gestures into speech and text. The sign language translation unit, for example, uses a generation AI to recognize sign language and translate it into speech and text. For example, if someone says "hello" in sign language, it translates it into speech as "hello." The sign language translation unit can also use a generation AI to recognize visual gestures and translate them into speech and text. This makes it possible to support communication with the hearing impaired by translating sign language and visual gestures.

[0033] The communication support system includes a subtitle display unit that displays translation results as subtitles in real time, and can display translation results as subtitles in real time. The subtitle display unit, for example, uses a generation AI to display translation results as subtitles in real time, allowing users to visually confirm them. For example, it displays subtitles for statements made during a meeting in real time. The subtitle display unit can also use a generation AI to build a system that displays translation results as subtitles in real time. This allows users to visually confirm the translation results by displaying them as subtitles in real time.

[0034] The translation unit can learn technical terminology and industry-specific words to provide more accurate translations. For example, the generation AI can learn technical terminology and industry-specific words to provide more accurate translations. For example, it can learn technical terminology in the medical industry to provide accurate translations. The translation unit can also use the generation AI to learn technical terminology and industry-specific words in real time and reflect this in the translation. This allows the system to learn technical terminology and industry-specific words to provide more accurate translations and improve work efficiency.

[0035] The communication support system is equipped with a documentation unit that automatically documents translation results and saves them as meeting minutes or reports. The documentation unit, for example, uses a generation AI to automatically document translation results and save them as meeting minutes. For example, it translates statements made during a meeting in real time and saves them as minutes. The documentation unit can also use a generation AI to automatically document translation results and save them as reports. This allows work efficiency to be improved by automatically documenting translation results and saving them as meeting minutes or reports.

[0036] The communication support system includes a video analysis unit that analyzes video conference footage and reflects the speaker's facial expressions and gestures in the translation. The video analysis unit, for example, uses a generation AI to analyze the video conference footage and reflect the speaker's facial expressions and gestures in the translation. For example, if the speaker is smiling while speaking, that emotion is reflected in the translation. The video analysis unit can also use a generation AI to analyze the video conference footage in real time and reflect the emotion in the translation. This allows for more natural communication by analyzing the video conference footage and reflecting the speaker's facial expressions and gestures in the translation.

[0037] The communication support system includes a chat providing unit that provides translation results not only as voice but also in chat format, and can provide translation results not only as voice but also in chat format. The chat providing unit, for example, uses a generation AI to provide translation results not only as voice but also in chat format, allowing the user to select. For example, it provides statements made during a meeting in both voice and chat format. The chat providing unit can also use a generation AI to build a system that provides translation results in chat format in real time. This makes it possible to support multiple means of communication by providing translation results not only as voice but also in chat format.

[0038] The translation unit can learn sport-specific tactics and terminology to provide more accurate translations. For example, the generative AI in the translation unit can learn sport-specific tactics and terminology to provide more accurate translations. For example, it can learn soccer tactical terminology and provide accurate translations. The translation unit can also use generative AI to learn sport-specific tactics and terminology in real time and reflect this in the translation. In this way, by learning sport-specific tactics and terminology, it can provide more accurate translations and facilitate smooth communication in sports situations.

[0039] The communication support system includes an audio guide unit that provides translation results as audio guide in real time, and can provide translation results as audio guide in real time. The audio guide unit, for example, uses a generation AI to provide translation results as audio guide in real time, allowing players to understand immediately. For example, instructions during a game are translated in real time and provided as audio guide. The audio guide unit can also use a generation AI to build a system that provides translation results as audio guide in real time. This allows players to understand immediately by providing translation results as audio guide in real time.

[0040] The communication support system is equipped with a commentary translation unit that translates game commentary and commentary and provides it to an international audience. The commentary translation unit, for example, uses a generative AI to translate game commentary in real time and provide it to an international audience. For example, it translates a soccer game commentary from English to Japanese. The commentary translation unit can also use a generative AI to translate game commentary in real time and provide it to viewers. This makes it possible to promote international understanding of sports by translating game commentary and commentary and providing it to an international audience.

[0041] The communication support system is equipped with a device display unit that displays the translation results on a smartphone or wearable device, and can display the translation results on the smartphone or wearable device. For example, the device display unit uses the generation AI to display the translation results on a smartphone so that players can easily check them. For example, it displays instructions during a game on the smartphone. The device display unit can also use the generation AI to display the translation results on the wearable device in real time. This allows players to easily check the translation results by displaying them on a smartphone or wearable device.

[0042] The translation unit can learn the culture and customs of the travel destination and suggest appropriate communication methods. For example, the translation unit uses a generation AI to learn the culture and customs of the travel destination and suggest appropriate communication methods. For example, it can learn Japanese culture and customs and suggest appropriate ways to greet people. The translation unit can also use generation AI to learn the culture and customs of the travel destination in real time and suggest communication methods. This allows travelers to communicate smoothly by learning the culture and customs of the travel destination and suggesting appropriate communication methods.

[0043] The communication support system includes an audio guide unit that provides translation results as an audio guide and explains tourist spots and historical background. The audio guide unit, for example, uses a generation AI to provide translation results as an audio guide and explains tourist spots and historical background. For example, the audio guide unit translates tourist spot information in real time and provides it as an audio guide. The audio guide unit can also use a generation AI to build a system that provides translation results as an audio guide in real time. This allows travelers to gain a deeper understanding by providing translation results as an audio guide and explaining tourist spots and historical background.

[0044] The communication support system is equipped with a map translation unit that translates maps and traffic information of the travel destination, allowing travelers to travel smoothly. The map translation unit, for example, uses a generation AI to translate maps of the travel destination, allowing travelers to travel smoothly. For example, it translates local maps in real time and provides them to travelers. The map translation unit can also use a generation AI to translate traffic information of the travel destination in real time and provide it to travelers. This allows travelers to travel smoothly by translating maps and traffic information of the travel destination.

[0045] The communication support system is equipped with an AR display unit that combines translation results with AR technology to visually provide local information, and can combine translation results with AR technology to provide local information visually. The AR display unit, for example, uses a generation AI to combine translation results with AR technology to visually provide local information. For example, it displays tourist destination information in AR and provides it to travelers. The AR display unit can also use a generation AI to build a system that displays translation results in real time in combination with AR technology. This allows travelers to understand the information more intuitively by combining translation results with AR technology and providing local information visually.

[0046] The emotion estimation unit can learn the user's goals and dreams and provide advice and support based on them. For example, the emotion estimation unit uses a generation AI to learn the user's goals and dreams and provide advice and support based on them. For example, if the user is aiming for an international career, the emotion estimation unit can provide learning resources for that. The emotion estimation unit can also use the generation AI to learn the user's goals and dreams in real time and provide advice and support. This allows the user to get closer to their ideal self by learning the user's goals and dreams and providing advice and support based on them.

[0047] The translation unit can promote intercultural exchange and new challenges, and support the user's growth. For example, the translation unit can use the generative AI's translation function to promote intercultural exchange and support the user's growth. For example, it can support online interactions with people who speak different languages. The translation unit can also use the generative AI to promote new challenges and support the user's growth. This promotes intercultural exchange and new challenges, and supports the user's growth, allowing the user to get closer to their ideal self.

[0048] The emotion estimation unit can analyze the user's skills and experience and suggest appropriate learning resources and training programs. For example, the emotion estimation unit uses a generative AI to analyze the user's skills and experience and suggest appropriate learning resources. For example, if a user wants to learn programming, the emotion estimation unit can suggest an appropriate online course. The emotion estimation unit can also use the generative AI to analyze the user's skills and experience in real time and suggest training programs. This allows the user to become closer to their ideal self by analyzing the user's skills and experience and suggesting appropriate learning resources and training programs.

[0049] The translation unit can provide opportunities to participate in international networking events and communities. The translation unit can, for example, use the translation function of the generative AI to provide opportunities to participate in international networking events. For example, it can support interactions with participants who speak different languages. The translation unit can also use the generative AI to provide opportunities to participate in international communities. This allows users to get closer to their ideal selves by providing opportunities to participate in international networking events and communities.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The communication support system may include a health management unit that monitors the user's health condition and suggests appropriate health management methods. For example, the system may monitor the user's heart rate and blood pressure, and recommend a doctor's consultation if any abnormalities are detected. The system may also monitor the user's exercise volume and suggest an appropriate exercise plan. This allows the system to monitor the user's health condition and suggest appropriate health management methods, thereby helping the user maintain their health.

[0052] The communication support system may include a learning support unit that monitors the user's learning progress and suggests appropriate learning resources. For example, if the user is struggling in a particular area, additional learning resources related to that area may be provided. Also, if the user has achieved a particular goal, resources for progressing to the next step may be suggested. In this way, by monitoring the user's learning progress and suggesting appropriate learning resources, the user's learning effectiveness can be improved.

[0053] The communication support system may include a schedule management unit that manages the user's schedule and provides reminders at appropriate times. For example, the system may set reminders so that the user does not forget an important meeting. The system may also suggest appropriate break times based on the user's schedule. In this way, the system can support the user's time management by managing the user's schedule and providing reminders at appropriate times.

[0054] The communication support system may also include a hobby suggestion unit that suggests appropriate events and activities based on the user's hobbies and interests. For example, if the user is interested in music, nearby concerts can be suggested. Also, if the user is interested in sports, local sporting events can be suggested. This makes it possible to enrich the user's life by suggesting appropriate events and activities based on the user's hobbies and interests.

[0055] The communication support system may include a purchase suggestion unit that analyzes the user's purchase history and suggests appropriate products and services. For example, related products may be suggested based on products the user has previously purchased. Specific services may also be suggested based on the user's purchase history. This makes it possible to improve the user's purchasing experience by analyzing the user's purchase history and suggesting appropriate products and services.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The translation unit translates the user's input. For example, it translates text or speech entered by the user into another language. It can also translate in real time using generative AI. For example, it uses text generation AI (e.g., LLM) to translate the entered text into another language. Step 2: The speech generation unit converts the translation results from the translation unit into speech. For example, it uses speech synthesis technology to convert text into speech. Alternatively, it can use generation AI to convert the translation results into natural-sounding speech. For example, it uses multimodal generation AI to convert the translation results into speech. Step 3: The emotion estimation unit estimates the user's emotion. For example, it can estimate the emotion by analyzing the user's facial expression and voice. It can also estimate the emotion from the user's text using a generation AI. For example, it can estimate the emotion from the user's text using a text generation AI.

[0058] (Example 2) The communication support system according to the embodiment of the present invention is a system that uses generative AI to remove language barriers and enable people all over the world to communicate smoothly. As a result, the communication support system can remove language barriers and support users so that they can be active all over the world.

[0059] A communication support system according to an embodiment includes a translation unit, a speech generation unit, and an emotion estimation unit. The translation unit translates a user's input. For example, the translation unit translates text input by a user into another language. The translation unit can also translate speech input by a user into another language. The translation unit can also translate the input language in real time using a generation AI. For example, the generation AI translates the input text into another language using a text generation AI (e.g., LLM). The speech generation unit converts the result of the translation by the translation unit into speech. For example, the speech generation unit uses speech synthesis technology to convert text into speech. The speech generation unit can also convert the translation result into natural-sounding speech using the generation AI. For example, the generation AI converts the translation result into speech using a multimodal generation AI. The emotion estimation unit estimates the user's emotion. For example, the emotion estimation unit analyzes the user's facial expression to estimate the emotion. The emotion estimation unit can also analyze the user's speech to estimate the emotion. The emotion estimation unit can also estimate emotions from a user's text using a generation AI. For example, the generation AI estimates emotions from a user's text using a text generation AI. This allows the communication support system according to the embodiment to remove language barriers and achieve smooth communication.

[0060] The translation unit can learn the user's pronunciation and intonation and generate more natural translated speech. For example, the translation unit uses a generation AI to learn the user's pronunciation and intonation and generate translated speech optimized for each individual user. For example, the translation unit can learn the unique intonation used when a user speaks Japanese and reflect that intonation when translating into English. The translation unit can also use the generation AI to learn the user's pronunciation and intonation in real time and reflect it in the translated speech. This allows the translation unit to learn the user's pronunciation and intonation and generate more natural translated speech, thereby improving the quality of communication.

[0061] The translation unit can take cultural nuances and idiomatic expressions into consideration and provide more appropriate expressions. For example, the translation unit uses a generative AI to learn cultural nuances and idiomatic expressions and provide appropriate expressions when translating. For example, translating the Japanese phrase "otsukaresama" into English "Good job." The translation unit can also use generative AI to perform translations that take cultural background into consideration. For example, the generative AI references a database of cultural background and provides an appropriate translation. This allows for more appropriate translations to be provided by taking cultural nuances and idiomatic expressions into consideration, improving the quality of communication.

[0062] The emotion estimation unit performs translation that reflects the user's emotions and can also convey emotional nuances. For example, the emotion estimation unit uses a generation AI to estimate the user's emotions and performs translation that reflects those emotions. For example, if the user is angry, the emotion estimation unit provides a translation that reflects that emotion. The emotion estimation unit can also use the generation AI to estimate the user's emotions in real time and reflect them in the translation. This allows for translation that reflects the user's emotions and can also convey emotional nuances.

[0063] The communication support system is equipped with a sign language translation unit that translates sign language and visual gestures into speech and text. The sign language translation unit, for example, uses a generation AI to recognize sign language and translate it into speech and text. For example, if someone says "hello" in sign language, it translates it into speech as "hello." The sign language translation unit can also use a generation AI to recognize visual gestures and translate them into speech and text. This makes it possible to support communication with the hearing impaired by translating sign language and visual gestures.

[0064] The communication support system includes a subtitle display unit that displays translation results as subtitles in real time, and can display translation results as subtitles in real time. The subtitle display unit, for example, uses a generation AI to display translation results as subtitles in real time, allowing users to visually confirm them. For example, it displays subtitles for statements made during a meeting in real time. The subtitle display unit can also use a generation AI to build a system that displays translation results as subtitles in real time. This allows users to visually confirm the translation results by displaying them as subtitles in real time.

[0065] The emotion estimation unit can provide feedback to reduce the stress and anxiety the user feels about the translation result. For example, the emotion estimation unit uses a generation AI to estimate the user's emotions and provide feedback to reduce the stress and anxiety the user feels about the translation result. For example, if the user feels anxious, the emotion estimation unit can provide advice on how to relax. The emotion estimation unit can also use the generation AI to estimate the user's emotions in real time and provide feedback. This can reduce the user's psychological burden by providing feedback to reduce the stress and anxiety the user feels about the translation result.

[0066] The translation unit can learn technical terminology and industry-specific words to provide more accurate translations. For example, the generation AI can learn technical terminology and industry-specific words to provide more accurate translations. For example, it can learn technical terminology in the medical industry to provide accurate translations. The translation unit can also use the generation AI to learn technical terminology and industry-specific words in real time and reflect this in the translation. This allows the system to learn technical terminology and industry-specific words to provide more accurate translations and improve work efficiency.

[0067] The communication support system is equipped with a documentation unit that automatically documents translation results and saves them as meeting minutes or reports. The documentation unit, for example, uses a generation AI to automatically document translation results and save them as meeting minutes. For example, it translates statements made during a meeting in real time and saves them as minutes. The documentation unit can also use a generation AI to automatically document translation results and save them as reports. This allows work efficiency to be improved by automatically documenting translation results and saving them as meeting minutes or reports.

[0068] The emotion estimation unit can analyze the emotions of participants during a meeting and suggest areas for improvement in communication. For example, the emotion estimation unit uses a generation AI to analyze the emotions of participants during a meeting and suggest areas for improvement in communication. For example, if a participant is feeling stressed, the emotion estimation unit can identify the cause and suggest improvements. The emotion estimation unit can also use a generation AI to analyze the emotions of participants during a meeting in real time and suggest areas for improvement. This makes it possible to improve the quality of meetings by analyzing the emotions of participants during a meeting and suggesting areas for improvement in communication.

[0069] The communication support system includes a video analysis unit that analyzes video conference footage and reflects the speaker's facial expressions and gestures in the translation. The video analysis unit, for example, uses a generation AI to analyze the video conference footage and reflect the speaker's facial expressions and gestures in the translation. For example, if the speaker is smiling while speaking, that emotion is reflected in the translation. The video analysis unit can also use a generation AI to analyze the video conference footage in real time and reflect the emotion in the translation. This allows for more natural communication by analyzing the video conference footage and reflecting the speaker's facial expressions and gestures in the translation.

[0070] The communication support system includes a chat providing unit that provides translation results not only as voice but also in chat format, and can provide translation results not only as voice but also in chat format. The chat providing unit, for example, uses a generation AI to provide translation results not only as voice but also in chat format, allowing the user to select. For example, it provides statements made during a meeting in both voice and chat format. The chat providing unit can also use a generation AI to build a system that provides translation results in chat format in real time. This makes it possible to support multiple means of communication by providing translation results not only as voice but also in chat format.

[0071] The emotion estimation unit can analyze the tone of emails and documents and make suggestions to revise them to appropriate expressions. For example, the emotion estimation unit uses a generative AI to analyze the tone of emails and documents and make suggestions to revise them to appropriate expressions. For example, it can suggest revising an email with a negative tone to a more positive one. The emotion estimation unit can also use a generative AI to analyze the tone of emails and documents in real time and make suggestions to revise them. This makes it possible to improve the quality of communication by analyzing the tone of emails and documents and making suggestions to revise them to appropriate expressions.

[0072] The translation unit can learn sport-specific tactics and terminology to provide more accurate translations. For example, the generative AI in the translation unit can learn sport-specific tactics and terminology to provide more accurate translations. For example, it can learn soccer tactical terminology and provide accurate translations. The translation unit can also use generative AI to learn sport-specific tactics and terminology in real time and reflect this in the translation. In this way, by learning sport-specific tactics and terminology, it can provide more accurate translations and facilitate smooth communication in sports situations.

[0073] The communication support system includes an audio guide unit that provides translation results as audio guide in real time, and can provide translation results as audio guide in real time. The audio guide unit, for example, uses a generation AI to provide translation results as audio guide in real time, allowing players to understand immediately. For example, instructions during a game are translated in real time and provided as audio guide. The audio guide unit can also use a generation AI to build a system that provides translation results as audio guide in real time. This allows players to understand immediately by providing translation results as audio guide in real time.

[0074] The emotion estimation unit can analyze the emotions of a player and provide feedback to increase motivation. For example, the emotion estimation unit uses the generation AI to analyze the emotions of a player and provide feedback to increase motivation. For example, if a player is feeling anxious, the emotion estimation unit can provide an encouraging message. The emotion estimation unit can also use the generation AI to analyze the emotions of a player in real time and provide feedback. This makes it possible to improve the player's performance by analyzing the player's emotions and providing feedback to increase motivation.

[0075] The communication support system is equipped with a commentary translation unit that translates game commentary and commentary and provides it to an international audience. The commentary translation unit, for example, uses a generative AI to translate game commentary in real time and provide it to an international audience. For example, it translates a soccer game commentary from English to Japanese. The commentary translation unit can also use a generative AI to translate game commentary in real time and provide it to viewers. This makes it possible to promote international understanding of sports by translating game commentary and commentary and providing it to an international audience.

[0076] The communication support system is equipped with a device display unit that displays the translation results on a smartphone or wearable device, and can display the translation results on the smartphone or wearable device. For example, the device display unit uses the generation AI to display the translation results on a smartphone so that players can easily check them. For example, it displays instructions during a game on the smartphone. The device display unit can also use the generation AI to display the translation results on the wearable device in real time. This allows players to easily check the translation results by displaying them on a smartphone or wearable device.

[0077] The emotion estimation unit can monitor the stress levels of players during a match and provide appropriate advice. For example, the emotion estimation unit uses the generation AI to monitor the stress levels of players during a match and provide appropriate advice. For example, if a player is feeling stressed, the emotion estimation unit can provide advice to help them relax. The emotion estimation unit can also use the generation AI to monitor the stress levels of players during a match in real time and provide advice. This makes it possible to improve a player's performance by monitoring the stress levels of players during a match and providing appropriate advice.

[0078] The translation unit can learn the culture and customs of the travel destination and suggest appropriate communication methods. For example, the translation unit uses a generation AI to learn the culture and customs of the travel destination and suggest appropriate communication methods. For example, it can learn Japanese culture and customs and suggest appropriate ways to greet people. The translation unit can also use generation AI to learn the culture and customs of the travel destination in real time and suggest communication methods. This allows travelers to communicate smoothly by learning the culture and customs of the travel destination and suggesting appropriate communication methods.

[0079] The communication support system includes an audio guide unit that provides translation results as an audio guide and explains tourist spots and historical background. The audio guide unit, for example, uses a generation AI to provide translation results as an audio guide and explains tourist spots and historical background. For example, the audio guide unit translates tourist spot information in real time and provides it as an audio guide. The audio guide unit can also use a generation AI to build a system that provides translation results as an audio guide in real time. This allows travelers to gain a deeper understanding by providing translation results as an audio guide and explaining tourist spots and historical background.

[0080] The emotion estimation unit can suggest relaxation methods to reduce the traveler's anxiety and tension. For example, the generation AI of the emotion estimation unit suggests relaxation methods to reduce the traveler's anxiety and tension. For example, if a traveler is feeling anxious, the emotion estimation unit can suggest breathing techniques to help them relax. The emotion estimation unit can also use the generation AI to estimate the traveler's anxiety and tension in real time and suggest relaxation methods. This allows the traveler to enjoy their trip with peace of mind by suggesting relaxation methods to reduce the traveler's anxiety and tension.

[0081] The communication support system is equipped with a map translation unit that translates maps and traffic information of the travel destination, allowing travelers to travel smoothly. The map translation unit, for example, uses a generation AI to translate maps of the travel destination, allowing travelers to travel smoothly. For example, it translates local maps in real time and provides them to travelers. The map translation unit can also use a generation AI to translate traffic information of the travel destination in real time and provide it to travelers. This allows travelers to travel smoothly by translating maps and traffic information of the travel destination.

[0082] The communication support system is equipped with an AR display unit that combines translation results with AR technology to visually provide local information, and can combine translation results with AR technology to provide local information visually. The AR display unit, for example, uses a generation AI to combine translation results with AR technology to visually provide local information. For example, it displays tourist destination information in AR and provides it to travelers. The AR display unit can also use a generation AI to build a system that displays translation results in real time in combination with AR technology. This allows travelers to understand the information more intuitively by combining translation results with AR technology and providing local information visually.

[0083] The emotion estimation unit can suggest tourist spots based on the traveler's interests and concerns. For example, the generation AI of the emotion estimation unit suggests tourist spots based on the traveler's interests and concerns. For example, if a traveler is interested in history, historical tourist spots will be suggested. The emotion estimation unit can also use the generation AI to analyze the traveler's interests and concerns in real time and suggest tourist spots. This allows the traveler to enjoy a more fulfilling trip by suggesting tourist spots based on the traveler's interests and concerns.

[0084] The emotion estimation unit can learn the user's goals and dreams and provide advice and support based on them. For example, the emotion estimation unit uses a generation AI to learn the user's goals and dreams and provide advice and support based on them. For example, if the user is aiming for an international career, the emotion estimation unit can provide learning resources for that. The emotion estimation unit can also use the generation AI to learn the user's goals and dreams in real time and provide advice and support. This allows the user to get closer to their ideal self by learning the user's goals and dreams and providing advice and support based on them.

[0085] The translation unit can promote intercultural exchange and new challenges, and support the user's growth. For example, the translation unit can use the generative AI's translation function to promote intercultural exchange and support the user's growth. For example, it can support online interactions with people who speak different languages. The translation unit can also use the generative AI to promote new challenges and support the user's growth. This promotes intercultural exchange and new challenges, and supports the user's growth, allowing the user to get closer to their ideal self.

[0086] The emotion estimation unit can provide feedback to increase the user's motivation and confidence. For example, the emotion estimation unit uses the generation AI to estimate the user's emotions and provide feedback to increase the user's motivation and confidence. For example, if the user is feeling down, the emotion estimation unit can provide an encouraging message. The emotion estimation unit can also use the generation AI to estimate the user's emotions in real time and provide feedback. This allows the user to get closer to their ideal self by providing feedback to increase the user's motivation and confidence.

[0087] The emotion estimation unit can analyze the user's skills and experience and suggest appropriate learning resources and training programs. For example, the emotion estimation unit uses a generative AI to analyze the user's skills and experience and suggest appropriate learning resources. For example, if a user wants to learn programming, the emotion estimation unit can suggest an appropriate online course. The emotion estimation unit can also use the generative AI to analyze the user's skills and experience in real time and suggest training programs. This allows the user to become closer to their ideal self by analyzing the user's skills and experience and suggesting appropriate learning resources and training programs.

[0088] The translation unit can provide opportunities to participate in international networking events and communities. The translation unit can, for example, use the translation function of the generative AI to provide opportunities to participate in international networking events. For example, it can support interactions with participants who speak different languages. The translation unit can also use the generative AI to provide opportunities to participate in international communities. This allows users to get closer to their ideal selves by providing opportunities to participate in international networking events and communities.

[0089] The emotion estimation unit can suggest relaxation and stress management methods according to the user's emotional state. For example, the emotion estimation unit uses a generation AI to estimate the user's emotional state and suggest relaxation methods. For example, if the user is feeling stressed, it can provide relaxing music. The emotion estimation unit can also use the generation AI to analyze the user's emotional state in real time and suggest stress management methods. This allows the user to become closer to their ideal self by suggesting relaxation and stress management methods according to the user's emotional state.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The communication support system may include a music providing unit that estimates the user's emotions and provides appropriate music to the user based on the estimated emotions. For example, if the user is feeling stressed, relaxing music may be provided. Alternatively, if the user is feeling happy, upbeat music may be provided to further enhance the user's emotions. In this way, the user's psychological state can be improved by providing music that matches the user's emotions.

[0092] The communication support system may include a feedback unit that estimates the user's emotions and provides appropriate feedback to the user based on the estimated emotions. For example, if the user feels anxious, the feedback unit may provide advice to help the user relax. If the user feels confident, the feedback unit may provide an encouraging message to further increase the user's confidence. In this way, by providing feedback according to the user's emotions, the user's psychological state can be improved.

[0093] The communication support system may include an exercise suggestion unit that estimates the user's emotions and suggests appropriate exercises and stretches to the user based on the estimated emotions. For example, if the user is feeling stressed, the unit may suggest relaxing yoga poses. Also, if the user is feeling energetic, the unit may suggest exercises to release that energy. In this way, by suggesting exercises and stretches that correspond to the user's emotions, the user's physical condition can be improved.

[0094] The communication support system may include a food suggestion unit that estimates the user's emotions and suggests appropriate meals and drinks to the user based on the estimated emotions. For example, if the user is tired, the unit may suggest meals to replenish energy. Also, if the user wants to relax, the unit may suggest herbal tea with a relaxing effect. In this way, by suggesting meals and drinks according to the user's emotions, the user's physical condition can be improved.

[0095] The communication support system may include a relaxation suggestion unit that estimates the user's emotions and suggests appropriate relaxation methods to the user based on the estimated emotions. For example, if the user is feeling stressed, the unit may suggest meditation or deep breathing. If the user wants to relax, the unit may suggest aromatherapy. In this way, the system can suggest relaxation methods according to the user's emotions, thereby improving the user's psychological state.

[0096] The communication support system may include a health management unit that monitors the user's health condition and suggests appropriate health management methods. For example, the system may monitor the user's heart rate and blood pressure, and recommend a doctor's consultation if any abnormalities are detected. The system may also monitor the user's exercise volume and suggest an appropriate exercise plan. This allows the system to monitor the user's health condition and suggest appropriate health management methods, thereby helping the user maintain their health.

[0097] The communication support system may include a learning support unit that monitors the user's learning progress and suggests appropriate learning resources. For example, if the user is struggling in a particular area, additional learning resources related to that area may be provided. Also, if the user has achieved a particular goal, resources for progressing to the next step may be suggested. In this way, by monitoring the user's learning progress and suggesting appropriate learning resources, the user's learning effectiveness can be improved.

[0098] The communication support system may include a schedule management unit that manages the user's schedule and provides reminders at appropriate times. For example, the system may set reminders so that the user does not forget an important meeting. The system may also suggest appropriate break times based on the user's schedule. In this way, the system can support the user's time management by managing the user's schedule and providing reminders at appropriate times.

[0099] The communication support system may also include a hobby suggestion unit that suggests appropriate events and activities based on the user's hobbies and interests. For example, if the user is interested in music, nearby concerts can be suggested. Also, if the user is interested in sports, local sporting events can be suggested. This makes it possible to enrich the user's life by suggesting appropriate events and activities based on the user's hobbies and interests.

[0100] The communication support system may include a purchase suggestion unit that analyzes the user's purchase history and suggests appropriate products and services. For example, related products may be suggested based on products the user has previously purchased. Specific services may also be suggested based on the user's purchase history. This makes it possible to improve the user's purchasing experience by analyzing the user's purchase history and suggesting appropriate products and services.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The translation unit translates the user's input. For example, it translates text or speech entered by the user into another language. It can also translate in real time using generative AI. For example, it uses text generation AI (e.g., LLM) to translate the entered text into another language. Step 2: The speech generation unit converts the translation results from the translation unit into speech. For example, it uses speech synthesis technology to convert text into speech. Alternatively, it can use generation AI to convert the translation results into natural-sounding speech. For example, it uses multimodal generation AI to convert the translation results into speech. Step 3: The emotion estimation unit estimates the user's emotion. For example, it can estimate the emotion by analyzing the user's facial expression and voice. It can also estimate the emotion from the user's text using a generation AI. For example, it can estimate the emotion from the user's text using a text generation AI.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a translation unit for translating user input; a speech generation unit that converts the translation result by the translation unit into speech; a feeling estimation unit that estimates the feeling of the user, A system characterized by:

2. It is equipped with a sign language translation unit that translates sign language and visual gestures, The sign language translation unit Translate sign language and visual gestures into speech and text 2. The system of claim 1.

3. The translation unit Learns technical terms and industry-specific language to provide more accurate translations 2. The system of claim 1.

4. The translation unit Learns sport-specific tactics and terminology to provide more accurate translations 2. The system of claim 1.

5. The translation unit Learn about the culture and customs of your destination and suggest appropriate ways to communicate 2. The system of claim 1.

6. The emotion estimation unit Learn about your goals and dreams and provide advice and support based on them 2. The system of claim 1.

7. The emotion estimation unit Translating text that reflects the user's emotions and conveys emotional nuances 2. The system of claim 1.

8. The emotion estimation unit Offer relaxation techniques to reduce traveler anxiety and tension 2. The system of claim 1.

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